System and method for fluctuating renewable energy-battery optimization to improve battery life-time

ABSTRACT

A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/699,317, filed Jul. 17, 2018 and entitled “System and Method forFluctuating Renewable Energy-Battery Optimization to Improve BatteryLife-Time,” which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to microgrid technology. Moreparticularly, the present disclosure relates to optimization of energyuse and distribution in renewable energy microgrids.

BACKGROUND

Power derived from renewable energy sources such as solar and wind isbecoming increasingly relied upon. Renewable energy sources may havelimitations that impede them from becoming widespread, low-cost,efficient, and continually viable sources of electricity. For example,renewable energy sources can be inherently unreliable, owing to factorssuch as changes in the time of day and variations in weather conditionsthat mean that maximized performance of components for each resource isvery difficult to manage.

Further, renewable energy sources face storage issues as electricitygrids may have limited inherent facility for storing electrical energy.This may require power to be generated constantly to meet uncertaindemand, which often results in over-generation (and hence wasted energy)and sometimes results in under-generation (and hence power failures).Additionally, there is limited facility for storing electrical energy atthe point of generation.

Microgrids are localized group of electricity sources and loads thatnormally operate connected to and synchronous with the traditional widearea synchronous grid (macrogrid), but can also disconnect to “islandmode”- and function autonomously as required. In this way, a microgridcan effectively integrate various sources of distributed generation(DG), especially Renewable Energy Sources (RES), and can supplyemergency power, changing between island and connected modes.

Control and protection are important elements for microgrids. Due tolimitations of renewable energy sources, certain isolated microgridswith reduced tolerance for energy disruptions are less likely to userenewable energy sources. These microgrids are becoming increasinglyprevalent in remote areas and enterprise campuses that requirecontinuous operation with no tolerance for energy disruption.Conventionally, to avoid negative impacts on such microgrids, renewableenergy sources are curtailed by operators, thus increasing environmentaland economical energy costs.

Certain microgrids may also use Battery Energy Storage Systems (BESS)which store energy when there is sufficient renewable energy generationand release power when renewable energy generation is insufficient.

However, there exists a need for improved systems and methods ofproducing, storing, transmitting, distributing and delivering energy sothat the needs of power customers can be satisfied from renewable energysources.

SUMMARY

The systems and methods described herein address treatment of mediumterm fluctuating renewable energy output, including of wind and solarrenewable energy systems, that can jeopardize operational constraints ofdiesel engines, leading to potential engine failures, in isolatedmicrogrids with no tolerance for energy disruption.

Some embodiments described herein can reduce the renewable energyproduction curtailment in such systems through the use of a noveloptimal control mechanism for Battery Energy Storage Systems (BESS). Inat least some embodiments, a novel two-phase forecast and controlplatform (an external controller) can be used to supervise the BESScharge and discharge to mitigate the effects of renewable fluctuations,while minimizing the impact on the BESS cycling and thus life-time.

In one aspect there is disclosed a system for optimizing energy. Thesystem may collect information from an information collector dataincluding energy usage and storage data of at least one renewable energygeneration system and battery energy storage system (BESS). The systemmay identify historical events that result in curtailment of renewableenergy production, determine whether there is a curtailment of renewableenergy production based at least on one historical event, supervise thecharge and discharge cycles of the at least one BESS. The system alsoconsiders generators (such as diesel generators) minimum up/down timeand uses the BESS to ensure such operational constraints are satisfied.

In another aspect there is a method of optimizing energy. The methodincludes collecting information from an information collector dataincluding energy usage and storage data of at least one renewable energygeneration system and battery energy storage system (BESS). The methodfurther includes identifying historical events that result incurtailment of renewable energy production, determining whether there isa curtailment of renewable energy production based at least on onehistorical event and supervising the charge and discharge cycles of theat least one BESS. The system also considers generators (such as dieselgenerators) minimum up/down time and uses the BESS to ensure suchoperational constraints are satisfied.

In another aspect, there is disclosed a computer program product foroptimizing energy production, the computer program product comprising acomputer readable medium storing program code, wherein the program code,when run on a computer, causes the computer to: receive informationcollector data from at least one information collector, the informationcollector data comprising energy usage and storage data of at least onerenewable energy generation system and battery energy storage system(BESS); identify historical events that result in curtailment ofrenewable energy production; determine whether there is a curtailment ofrenewable energy production of the at least one renewable energygeneration system based at least on the historical events; supervise thecharge and discharge cycles of the at least one BESS; the system alsoconsiders the generators (such as diesel generators) minimum up/downtime and uses the BESS to ensure such operational constraints aresatisfied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a plot of power generation over time for a renewable energygeneration and storage system in one mode of operation;

FIG. 2 is a plot of power generation over time for a renewable energygeneration and storage system in another mode of operation;

FIG. 3 is a plot of power generation over time for a renewable energygeneration and storage system in another mode of operation;

FIG. 4 is a block diagrammatic view of an example microgrid optimizationsystem in accordance with one embodiment of the present disclosure;

FIG. 5 is a flow diagram of a method of optimizing a microgrid inaccordance with one embodiment of the present disclosure;

FIG. 6 is a plot showing the optimization of a microgrid system overtime;

FIG. 7 is a flow diagram of a method of optimizing a microgrid inaccordance with another embodiment of the present disclosure;

FIG. 8 is a plot showing power generation against renewable energyoutput standard deviation of an example microgrid optimization system;

FIG. 9 is a plot showing power generation against renewable energyoutput standard deviation of an example microgrid optimization system;and

FIG. 10 is a plot showing power generation against the state of chargeof an example microgrid optimization system.

DETAILED DESCRIPTION OF EMBODIMENTS

Referring to FIGS. 1 to 3, example power generation profiles ofconventional renewable energy microgrids are shown. The profiles shownin FIGS. 1 to 3 represent actual measurements obtained from windturbines. In microgrids in which there is minimal tolerance for energydisruption, the state of charge (SOC) of the BESS tends to be kept atthe maximum level during normal operation. In these cases, the BESSprimary controller, generally has a sampling rate in the range of onehundred milliseconds, and can receive the following set-points:

P^(Max): The limit for the power injection at the point of commoncoupling (PCC), beyond which the BESS charges to maintain the injectionlevel at the limit.

P^(Min): The limit for the power injection at the PCC, below which theBESS discharges to maintain the injection level at the limit.

R_(ch,dch): The power rate of the BESS controlling thecharging/discharging speed.

R_(Thresh): The threshold for the rate of renewable energy outputchanges, beyond which the BESS charges or discharges. By default, thisthreshold is set relatively high, avoiding continuouscharging/discharging of the BESS due to life-time concerns.

As seen in FIGS. 1 to 3, there are generally three operational statesfor such systems. FIG. 1 shows a first operational state in which thereis a sustained renewable energy output decrease. The sustained decreaseis represented by the dotted box in FIG. 1. In the microgrids describedabove, the BESS primary controller compensates for such a power drop,through the appropriate choice of P^(Min), thus giving the dieselengines enough time to ramp up or turn on.

FIG. 2 shows a second operational state in which the renewable energyoutput rapidly decreases. This sudden decrease is shown by the dottedbox. In such cases, the governors of the diesel engines would addressthis problem by increasing the production of the engine, thus the BESSwould not discharge any power, unless the injected power falls belowP^(Min).

In cases where there are large and sustained renewable energy outputfluctuations (shown in the dotted box in FIG. 3), this leads toconsiderable mechanical stress on diesel engines and jeopardizes theoperational constraints such as the minimum up/down time of the engine.

Referring to FIG. 4, an example microgrid optimization engine 102 andmethod are disclosed. The system includes an optimization engine 102.The system 100 includes a processor 106 and memory 110. The memorycontains instructions that can be executed by the processor and whichwhen executed, cause the system 100 to perform the steps describedherein. For example, in at least some embodiments, the optimizationengine 102 is configured to obtain historical renewable output data toobtain a profile of the operational states shown in FIGS. 1 to 3.

The system also includes a battery energy storage system 104 thatincludes a primary controller as described above and an externalcontroller 108 that can be used to supervise the BESS charge anddischarge to mitigate the effects of fluctuations of renewable energygeneration sets/devices 112 (for example, wind turbines, solar panels)on generators 114. Generators 114 may include diesel generators,gas-powered generators, combined heat and power generators, and certainfuel cells. The system 100 also includes one or more industrial loads115 such as residential or industrial loads that are operably connectedto and draw power from one or more of the generation devices 112 as wellas control and monitoring user interfaces 116. Interfaces 116 mayinclude monitors, input/output devices such as touchscreen, keyboards,microphone for voice input, and the like.

In operation, renewable energy generation and BESS measurements andstatus data including power generation output can be received from theBESS 104 and the renewable energy generation sets 112 and feedback intooptimization engine 102 (signal 1) by means of one or more communicationsubsystems within processor 106 or by means of shared memory 110.Similarly, weather data including weather forecasts can be received fromone or more weather stations 118 and transmitted to optimization engine102 (signal 2). Weather stations 118 can be external to the system 100.The data can then be used to calculate the optimization function asdescribed further herein.

In particular, optimization engine 102 can aggregate microgrid dataobtained as input and send it as output to a central model formationsystem 120 (signal 3) that is configured to execute the algorithmsdescribed herein by means of artificial intelligence (AI) assistedoptimization and calculation. The output can then be fed back to theoptimization engine 102 by the central model formation system 120(signal 4). Consumer load and diesel generation measurements (signal 5)can be obtained from the loads 115 and generators 114 and input into theuser interfaces 116.

Subsequently, variability forecasts, and generation control signals(signal 6) can be output from the optimization engine 102 to the userinterfaces 116 and diesel generation and load measurements, alerts, andoperator over-ride controls (signal 7) can be output as instructions orcommands from the user interfaces 116 to the optimization engine 102.Finally, BESS and curtailment control signals (signal 8) are output fromthe user interfaces 116 to the BESS 102 and renewable energy devices 112based on instructions or calculations from the optimization engine 102.

In these microgrids, the parameters of interest are the renewable energyoutput standard deviation, σ_(wind), on a rolling renewable energysystem of length p, and a threshold standard deviation σ_(τ). Thehistorical events that result in renewable energy production curtailmentare first identified. These historical events are basically past windoutput fluctuations that resulted into wind output being curtailed bythe operator. Next, a threshold standard deviation σ_(τ). and length ρare determined based on the parameters of the system 100. In a windturbine system, a scenario in which the standard deviation of wind speedexceeds a threshold (σ_(Wind)>σ_(τ)) is the necessary and sufficientcondition for a renewable energy curtailment event.

Referring to FIGS. 8 to 9, energy efficiency droop diagrams aredisclosed showing the power generation against the standard deviation ofwind of an example BESS. As shown in FIG. 4, as the σ_(wind) increases,the ρ^(Max) decreases while the ρ^(Min) increases. As a result, thebattery starts to charge/discharge to alleviate the renewablefluctuations, thus yielding a lower σ_(Wind). Hence, the systemresembles a non-cooperative game involving ρ^(Max,Min) and σ_(Wind), andas a result, it will settle down to the so-called Nash Equilibrium withrespect to σ_(e), ρ_(e) ^(Max), and ρ_(e) ^(Min). Theoretically, σ_(e)is always less than σ_(τ). This tends to be true for continuousvariables. In practice, the proposed mechanism update rate depends ontechnical requirements and communication bandwidth.

Various droop mechanisms can be used. For example, mechanismscorresponding to droop diagrams shown in FIG. 8 can be any parabolicequation exhibiting similar characteristics. As an example, anappropriate ρ^(Max) can be calculated using the following equation:

$\begin{matrix}{P^{Max} = {{2\left( {P_{0}^{Max} - P_{\tau}} \right)\left( {\frac{1}{1 + e^{({\sigma - \sigma_{\tau}})}} - 0.5} \right)} + P_{\tau}}} & (1) \\{P^{Min} = {{{- 2}\left( {P_{0}^{Min} - P_{\tau}} \right)\left( {\frac{1}{1 + e^{({\sigma - \sigma_{\tau}})}} - 0.5} \right)} + P_{\tau}}} & (2)\end{matrix}$

In some embodiments, a droop mechanism that can be represented by apiece-wise linear diagram (as shown in FIG. 9) can be used. Theappropriate ρ^(Max) and ρ^(Min) can be calculated as a series of linearequations, as would be understood in the art.

Referring to FIG. 10, the state of charge (SOC) of the battery can becontrolled by a SOC vs. ρ_(τ) droop mechanism. As seen in FIG. 10, aparabolic choice of droop is proposed to keep the battery SOC closer tothe SOC_(Max). A parabolic equation similar to (2) can be used.Alternatively, a piece-wise linear droop, similar to the one representedby FIG. 9 can be used.

Obtaining the desired system performance depends on an appropriatechoice of σ_(τ), ρ, P₀ ^(Max), and P₀ ^(Min). These parameters depend onthe operating condition of system 100, including the number of operatingdiesel generators (gensets), their output level, and their minimumup/down time. In some embodiments, the choice of these values can beconservative and based on experimental studies on past data. In thesecases, a careful analysis of historical data, along with detailedsimulation studies, would be required to ensure the operationalstability and appropriate coordination with the primary controller ofBESS 104.

Referring to FIG. 5, a flow chart of a method 200 for determining anappropriate P₀ ^(Max,Min) is disclosed. The method ensures that thediesel gensets minimum up/down time is satisfied. In exampleembodiments, the method 200 is carried out more frequently than theexternal controller 108, and ideally in a comparable update rate to theprimary controller of BESS 104.

In FIG. 5, Flag_(Up,Dw) are two binary variables indicating if a dieselgenset is currently on/off and cannot change status, Counter_(Up,Dw) arevariables for the remaining time until which a unit cannot be turnedoff/on, T_(Up,Dw) ^(Min) are the minimum up/down time and units P_(Lim)^(Max,Min) are the set-point limits of P^(Max,Min) and P_(DW) ^(Min) arethe power injection limits at the Power control centre (PCC) beyondwhich a diesel genset has to turn off/on.

The optimization mechanism shown in FIG. 5 can be further improved byincluding a Model Predictive Control (MPC) optimizer, which relies onAI-assisted predictions and calculations. The first step in implementingthe MPC optimizer requires determining predictions of μ_(w) and σ_(w),the renewable energy output mean and standard deviation respectively,for a certain upcoming time horizon (e.g., 12 hours). The predictioninterval would bep, as defined previously. Based on the predicted meanand standard deviation, two other parameters,

and

can be predicted.

represents the percentage of the prediction interval during which therenewable output is higher than μ_(w), and

=1−

. It is assumed that the renewable energy output during the interval iseither

or

, satisfying the following system of equations:

σ_(W) ²=(

−μ_(W))²+(

−μ_(W))²  (3)

μ_(W)=

+(1−

)

  (4)

Based on the obtained values of

or

, it is possible to formulate a MPC optimizer using the followingfunction:

$\begin{matrix}{{Obj}\text{:}\mspace{14mu} \min \left\{ {{\alpha {\sum\limits_{k \in T}\; \left\lbrack {\left( {W_{\mathcal{H}_{k}} - P_{k}^{Max}} \right) + \left( {P_{k}^{Min} - W_{\mathcal{L}_{k}}} \right)} \right\rbrack}} + {\beta {\sum\limits_{k \in T}\left( {{SOC}_{Max} - {SOC}_{k}} \right)^{2}}}} \right\}} & (5)\end{matrix}$

S.t.

σ_(inj) _(k) ≤σ_(τ) ∀k∈T

SOC _(Min) ≤SOC _(k) ≤SOC _(Max) ∀k∈T

P _(Limit) ^(Min) ≤P _(k) ^(Min) ≤P _(k) ^(Max) ≤P _(Limit) ^(Max) ∀k∈T

γ_(k)(P _(k) ^(Max−)

⁾⁺⁽1−γ_(k))(

−P _(k) ^(Max))≥0 ∀k∈T

δ_(k)(

−P _(k) ^(Min))+(1−δ_(k))(P _(k) ^(Min−)

^()≥)0 ∀k∈T  (6)

In the model shown above, K is a time-interval that belongs to set oftime-intervals, T, within the optimization horizon. p^(Max) and p^(Min)are the decision variables. SOC_(Max,Min) are the maximum and minimumlimits of the SOC. P_(Limit) ^(Max,Min) are the set-point limits ofP^(Max,Min). α and β are arbitrary coefficients of the objectivefunction,

and

are the optimization parameters obtained from (3) and (4), and γ_(κ) andδ_(κ) are binary variables indicating if p^(Max,Min) are higher or lowerthan

·σinj_(κ) is the estimated standard deviation of the injected power atthe PCC during the time interval κ.

SOC_(κ) can be calculated as follows:

$\begin{matrix}\begin{matrix}{\sigma_{{inj}_{k}} \approx \left\{ \begin{matrix}{\left( {P_{k}^{Max} - \mu_{W_{k}}} \right)^{2} + \left( {P_{k}^{Min} - \mu_{W_{k}}} \right)^{2}} & {{{if}\mspace{14mu} \gamma_{k}} = {\delta_{k} = 0}} \\{\left( {P_{k}^{Max} - \mu_{W_{k}}} \right)^{2} + \left( {W_{\mathcal{L}_{k}} - \mu_{W_{k}}} \right)^{2}} & {{{if}\mspace{14mu} \gamma_{k}} = {{0\mspace{14mu} {and}\mspace{14mu} \delta_{k}} = 1}} \\{\left( {W_{\mathcal{H}_{k}} - \mu_{W_{k}}} \right)^{2} + \left( {P_{k}^{Min} - \mu_{W_{k}}} \right)^{2}} & {{{if}\mspace{14mu} \gamma_{k}} = {{1\mspace{11mu} {and}\mspace{14mu} \delta_{k}} = 0}} \\\sigma_{W_{k}} & {{{if}\mspace{14mu} \gamma_{k}} = {1 = {\delta_{k} = 0}}}\end{matrix} \right.} & {\forall{k \in T}}\end{matrix} & (7) \\\begin{matrix}{{SOC}_{k} = {{SOC}_{k - 1} + {k\left\lbrack {{\left( {1 - \gamma_{k}} \right)\left( {P_{k}^{Max} - \mu_{W_{k}}} \right)} + {\left( {1 - \delta_{k}} \right)\left( {P_{k}^{Min} - \mu_{W_{k}}} \right)}} \right\rbrack}}} & {\forall{k \in T}}\end{matrix} & (8)\end{matrix}$

As discussed above, such a MPC-based optimization is carried out for acertain time horizon with the time intervals of ρ multiples, as shown inFIG. 6.

A similar mechanism can be implemented to ensure that the diesel gensetsminimum up/down time is satisfied. For example, a sample technique isillustrated in FIG. 7. In some embodiments, the mechanism proposed inFIG. 7 is carried out more frequently than the external controller.

The advantage of the optimization engine 102 as described herein is thatthe ESS charging/discharging cycle is part of the objective function;thus, the technique proposed in this section attempts to minimize theESS cycle, hence maximizing battery life, while ensuring that therenewable energy output variability is within acceptable ranges.

As applicable, at least some of the described embodiments may similarlyapply to applications outside of wind generation systems such as solar,thermal, and the like.

In the described methods or block diagrams, the boxes may representevents, steps, functions, processes, modules, messages, and/orstate-based operations, etc. While some of the above examples have beendescribed as occurring in a particular order, it will be appreciated bypersons skilled in the art that some of the steps or processes may beperformed in a different order provided that the result of the changedorder of any given step will not prevent or impair the occurrence ofsubsequent steps.

Furthermore, some of the messages or steps described above may beremoved or combined in other embodiments, and some of the messages orsteps described above may be separated into a number of sub-messages orsub-steps in other embodiments. Even further, some or all of the stepsmay be repeated, as necessary. Elements described as methods or stepssimilarly apply to systems or subcomponents, and vice-versa.

Reference to such words as “sending” or “receiving” could beinterchanged depending on the perspective of the particular device.

The above-discussed embodiments are considered to be illustrative andnot restrictive. Embodiments described as methods would similarly applyto systems, and vice-versa.

Variations may be made to some embodiments, which may includecombinations and sub-combinations of any of the above. The variousembodiments presented above are merely examples and are in no way meantto limit the scope of this disclosure.

Variations of the innovations described herein will be apparent topersons of ordinary skill in the art, such variations being within theintended scope of the present disclosure. In particular, features fromone or more of the above-described embodiments may be selected to createalternative embodiments comprised of a sub-combination of features whichmay not be explicitly described above. In addition, features from one ormore of the above-described embodiments may be selected and combined tocreate alternative embodiments comprised of a combination of featureswhich may not be explicitly described above. Features suitable for suchcombinations and sub-combinations would be readily apparent to personsskilled in the art upon review of the present disclosure as a whole. Thesubject matter described herein intends to cover and embrace allsuitable changes in technology.

1. A system for energy optimization, the system comprising: a processor;a memory coupled to the processor, the memory storingcomputer-executable instructions that, when executed by the processingdevice, cause the system to: receive information collector data from atleast one information collector, the information collector datacomprising energy usage and storage data of a renewable energygeneration system that includes a battery energy storage system (BESS);identify historical events that result in curtailment of renewableenergy production; determine whether there is a curtailment of renewableenergy production of the renewable energy generation system based atleast on the historical events; supervise the charge and dischargecycles of the BESS; operate a generator operably connected to the BESSbased on the information collector data and historical events; andensure that a minimum up/down time of the generator is satisfied basedon controlling at least one parameter of the BESS.
 2. The system ofclaim 1 wherein the computer-executable instructions further cause thesystem to determine a model predictive control optimization function. 3.The system of claim 2 wherein the model predictive control optimizationfunction is represented at least partially by:${Obj}\text{:}\mspace{14mu} \min {\quad{\quad{\left\{ {{\alpha {\sum\limits_{k \in T}\; \left\lbrack {\left( {W_{\mathcal{H}_{k}} - P_{k}^{Max}} \right) + \left( {P_{k}^{Min} - W_{\mathcal{L}_{k}}} \right)} \right\rbrack}} + {\beta {\sum\limits_{k \in T}\left( {{SOC}_{Max} - {SOC}_{k}} \right)^{2}}}} \right\} \mspace{85mu} {S.t.\mspace{79mu} \begin{matrix}{\sigma_{{inj}_{k}} \leq \sigma_{\tau}} & {\forall{k \in T}}\end{matrix}}\mspace{85mu} \begin{matrix}{{SOC}_{Min} \leq {SOC}_{k} \leq {SOC}_{Max}} & {\forall{k \in T}}\end{matrix}\begin{matrix}{\mspace{79mu} {P_{Limit}^{Min} \leq P_{k}^{Min} \leq P_{k}^{Max} \leq P_{Limit}^{Max}}} & {\forall{k \in T}}\end{matrix}\begin{matrix}{\mspace{79mu} {{{\gamma_{k}\left( {P_{k}^{Max} - W_{\mathcal{H}_{k}}} \right)} + {\left( {1 - \gamma_{k}} \right)\left( {W_{\mathcal{H}_{k}} - P_{k}^{Max}} \right)}} \geq 0}} & {\forall{k \in T}}\end{matrix}\begin{matrix}{\mspace{85mu} {{{\delta_{k}\left( {W_{\mathcal{L}_{k}} - P_{k}^{Min}} \right)} + {\left( {1 - \delta_{k}} \right)\left( {P_{k}^{Min} - W_{\mathcal{L}_{k}}} \right)}} \geq 0}} & {\forall{k \in T}}\end{matrix}}}}$
 4. The system of claim 1 wherein the system isconfigured to operate in near real-time by operating using apredetermined time interval or less.
 5. The system of claim 1, whereinthe energy usage and storage data includes overall energy usage data,required energy reductions, required demand reductions, cost of energydata, and data regarding distribution of energy.
 6. The system of claim1 wherein the generator is a diesel generator.
 7. A method for energyoptimization comprising: receiving information collector data from atleast one information collector, the information collector datacomprising energy usage and storage data of a renewable energygeneration system that includes a battery energy storage system (BESS);identifying historical events that result in curtailment of renewableenergy production; determining whether there is a curtailment ofrenewable energy production of the renewable energy generation systembased at least on the historical events; supervising the charge anddischarge cycles of the BESS; operating a generator operably connectedto the BESS based on the information collector data and historicalevents; and ensuring that a minimum up/down time of the generator issatisfied based on controlling the BESS.
 8. The method of claim 7wherein the method is executed in near real-time, wherein near real-timeis operating using a pre-determined time interval or less.
 9. The methodof claim 7, further comprising determining a model predictive controloptimization function.
 10. The method of claim 9 wherein the modelpredictive control optimization function is represented at leastpartially by:${Obj}\text{:}\mspace{14mu} \min \left\{ {{\alpha {\sum\limits_{k \in T}\; \left\lbrack {\left( {W_{\mathcal{H}_{k}} - P_{k}^{Max}} \right) + \left( {P_{k}^{Min} - W_{\mathcal{L}_{k}}} \right)} \right\rbrack}} + {\beta {\sum\limits_{k \in T}\left( {{SOC}_{Max} - {SOC}_{k}} \right)^{2}}}} \right\}$     S.t.σ_(inj) _(k) ≤σ_(τ) ∀k∈TSOC _(Min) ≤SOC _(k) ≤SOC _(Max) ∀k∈TP _(Limit) ^(Min) ≤P _(k) ^(Min) ≤P _(k) ^(Max) ≤P _(Limit) ^(Max) ∀k∈Tγ_(k)(P _(k) ^(Max−)

⁾⁺⁽1−γ_(k))(

−P _(k) ^(Max))≥0 ∀k∈Tδ_(k)(

−P_(k) ^(Min))+(1−δ_(k))(P _(k) ^(Min−)

^()≥)0 ∀k∈T  (6)
 11. The method of claim 7, wherein the energy usage andstorage data includes overall energy usage data, required energyreductions, required demand reductions, cost of energy data, and dataregarding distribution of energy.
 12. The method of claim 7 wherein thegenerator is a diesel generator.
 13. A computer program product foroptimizing energy production, the computer program product comprising acomputer readable medium storing program code, wherein the program code,when run on a computer, causes the computer to: receive informationcollector data from at least one information collector, the informationcollector data comprising energy usage and storage data of a renewableenergy generation system that includes a battery energy storage system(BESS); identify historical events that result in curtailment ofrenewable energy production; determine whether there is a curtailment ofrenewable energy production of the renewable energy generation systembased at least on the historical events; supervise the charge anddischarge cycles of the BESS; operate a generator operably connected tothe BESS based on the information collector data and historical events;and ensuring that a minimum up/down time of the generator is satisfiedbased on controlling at least one parameter of the BESS.
 14. Thecomputer program product of claim 13 wherein the program code furthercauses the computer to determine a model predictive control optimizationfunction.
 15. The computer program product of claim 13 wherein the modelpredictive control optimization function is represented at leastpartially by:${Obj}\text{:}\mspace{14mu} \min \left\{ {{\alpha {\sum\limits_{k \in T}\; \left\lbrack {\left( {W_{\mathcal{H}_{k}} - P_{k}^{Max}} \right) + \left( {P_{k}^{Min} - W_{\mathcal{L}_{k}}} \right)} \right\rbrack}} + {\beta {\sum\limits_{k \in T}\left( {{SOC}_{Max} - {SOC}_{k}} \right)^{2}}}} \right\}$     S.t.σ_(inj) _(k) ≤σ_(T) ∀k∈TSOC _(Min) ≤SOC _(k) ≤SOC _(Max) ∀k∈TP _(Limit) ^(Min) ≤P _(k) ^(Min) ≤P _(k) ^(Max) ≤P _(Limit) ^(Max) ∀k∈Tγ_(k)(P _(k) ^(Max−)

⁾⁺⁽1−γ_(k))(

−P_(k) ^(Max))≥0 ∀k∈Tδ_(k)(

−P_(k) ^(Min))+(1−δ_(k))(P _(k) ^(Min−)

^()≥)0 ∀k∈T  (6)
 16. The computer program product of claim 13 whereinthe renewable energy generation system is configured to operate in nearreal-time by using a predetermined time interval or less.
 17. Thecomputer program product of claim 13, wherein the energy usage andstorage data includes overall energy usage data, required energyreductions, required demand reductions, cost of energy data, and dataregarding distribution of energy.
 18. The computer program product ofclaim 13 wherein the generator is a diesel generator.